{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/aramhur/Desktop/untitled folder/bjps koreaexp_output.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Feb 2018, 10:02:48

{com}. do "/var/folders/dc/pvb3ftwn0s77yg2dqmphjsxw0000gp/T//SD05397.000000"
{txt}
{com}. **** recode variables *****
. gen duty=pre_duty
{txt}(1,745 missing values generated)

{com}.         recode duty 0=. 1=1 2=0
{txt}(duty: 143 changes made)

{com}. 
.         
. gen mobileinterest=pre_mobileinterest
{txt}(1,748 missing values generated)

{com}.         recode mobileinterest 0=. 3/4=0 2=1 1=2
{txt}(mobileinterest: 349 changes made)

{com}.         
. gen polint=pre_polint
{txt}(1,743 missing values generated)

{com}.         recode polint 0=. 4=0 3=1 2=2 1=3
{txt}(polint: 177 changes made)

{com}.         
. gen female=sex
{txt}(1,744 missing values generated)

{com}.         recode female 1=0 2=1
{txt}(female: 335 changes made)

{com}. 
. gen ageyrs=age
{txt}(1,744 missing values generated)

{com}.         recode ageyrs 0=.
{txt}(ageyrs: 18 changes made)

{com}.         replace ageyrs=ageyrs+19
{txt}(335 real changes made)

{com}.         
. gen educ=educat
{txt}(1,745 missing values generated)

{com}.         recode educ 0=.
{txt}(educ: 18 changes made)

{com}.         recode educ 1/2=0 3/4=1 5/6=2 //hs or lower, college, graduate
{txt}(educ: 334 changes made)

{com}.                 
. recode nectime 0=. //in years
{txt}(nectime: 18 changes made)

{com}. 
. 
. **** experiment results ****
. //unmatched
. 
. ttest duty, by(treatment)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    233{col 22} .5879828{col 34} .0323144{col 46} .4932578{col 58} .5243157{col 70}   .65165
       {txt}1 {c |}{res}{col 12}    106{col 22} .6792453{col 34} .0455518{col 46} .4689841{col 58} .5889246{col 70}  .769566
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    339{col 22} .6165192{col 34} .0264476{col 46} .4869525{col 58} .5644965{col 70} .6685419
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0912625{col 34} .0569179{col 58}-.2032215{col 70} .0206966
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} -1.6034
{txt}Ho: diff = 0                                     degrees of freedom = {res}     337

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0549         {txt}Pr(|T| > |t|) = {res}0.1098          {txt}Pr(T > t) = {res}0.9451
{txt}
{com}. ttest vote, by(treatment)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}  1,073{col 22}  .751165{col 34} .0132046{col 46} .4325396{col 58} .7252551{col 70} .7770748
       {txt}1 {c |}{res}{col 12}  1,024{col 22} .8066406{col 34} .0123477{col 46} .3951253{col 58}  .782411{col 70} .8308703
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,097{col 22} .7782546{col 34} .0090739{col 46} .4155198{col 58} .7604599{col 70} .7960494
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0554757{col 34} .0181165{col 58}-.0910039{col 70}-.0199474
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} -3.0622
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2095

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0011         {txt}Pr(|T| > |t|) = {res}0.0022          {txt}Pr(T > t) = {res}0.9989
{txt}
{com}. 
. //matched control-treatment pairs
. 
. findit pscore //install pscore package via link
{txt}
{com}. 
. global treatment treatment
{txt}
{com}. global xlist ageyrs female nectime educ if pre_finish!=.
{txt}
{com}. global breps 100
{txt}
{com}. 
. pscore $treatment $xlist, pscore(pscore) blockid(myblock) comsup



{res}**************************************************** 
Algorithm to estimate the propensity score 
**************************************************** 


The treatment is treatment

  {txt}treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        244       68.35       68.35
{txt}          1 {c |}{res}        113       31.65      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        357      100.00



Estimation of the propensity score 

{txt}Iteration 0:   log likelihood = {res}-203.69212
{txt}Iteration 1:   log likelihood = {res}-197.72705
{txt}Iteration 2:   log likelihood = {res}-197.71949
{txt}Iteration 3:   log likelihood = {res}-197.71949

{txt}Probit regression                                 Number of obs   = {res}       329
                                                  {txt}LR chi2({res}4{txt})      = {res}     11.95
                                                  {txt}Prob > chi2     = {res}    0.0178
{txt}Log likelihood = {res}-197.71949                       {txt}Pseudo R2       = {res}    0.0293

{txt}{hline 13}{c TT}{hline 64}
   treatment {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
      ageyrs {c |}  {res} -.046595    .019362    -2.41   0.016    -.0845438   -.0086462
      {txt}female {c |}  {res} -.364257   .1977254    -1.84   0.065    -.7517915    .0232776
     {txt}nectime {c |}  {res} .0186112   .0211078     0.88   0.378    -.0227593    .0599817
        {txt}educ {c |}  {res}-.1484781   .1550632    -0.96   0.338    -.4523965    .1554403
       {txt}_cons {c |}  {res}  1.48897   .6602584     2.26   0.024     .1948876    2.783053
{txt}{hline 13}{c BT}{hline 64}



{res}Note: the common support option has been selected
The region of common support is [.13142924, .64101893]



Description of the estimated propensity score 
in region of common support 

                 {txt}Estimated propensity score
{hline 61}
      Percentiles      Smallest
 1%    {res} .1523349       .1314292
{txt} 5%    {res} .1775903       .1314292
{txt}10%    {res} .2028746       .1523349       {txt}Obs         {res}        329
{txt}25%    {res} .2472689       .1523349       {txt}Sum of Wgt. {res}        329

{txt}50%    {res} .3065335                      {txt}Mean          {res} .3104524
                        {txt}Largest       Std. Dev.     {res} .0866783
{txt}75%    {res} .3680351       .4955995
{txt}90%    {res} .4327304       .5030241       {txt}Variance      {res} .0075131
{txt}95%    {res} .4544885       .5399171       {txt}Skewness      {res} .3029043
{txt}99%    {res} .4955995       .6410189       {txt}Kurtosis      {res} 2.810238



****************************************************** 
Step 1: Identification of the optimal number of blocks 
Use option detail if you want more detailed output 
****************************************************** 


{txt}The final number of blocks is 4

This number of blocks ensures that the mean propensity score
is not different for treated and controls in each blocks



{res}********************************************************** 
Step 2: Test of balancing property of the propensity score 
Use option detail if you want more detailed output 
********************************************************** 


{txt}The balancing property is satisfied 


This table shows the inferior bound, the number of treated
and the number of controls for each block 

  Inferior {c |}
  of block {c |}       treatment
of pscore  {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
  .1314292 {c |}{res}        23          6 {txt}{c |}{res}        29 
{txt}        .2 {c |}{res}       175         69 {txt}{c |}{res}       244 
{txt}        .4 {c |}{res}        29         26 {txt}{c |}{res}        55 
{txt}        .6 {c |}{res}         0          1 {txt}{c |}{res}         1 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       227        102 {txt}{c |}{res}       329 

{txt}Note: the common support option has been selected


{res}******************************************* 
End of the algorithm to estimate the pscore 
******************************************* 
{txt}
{com}. 
. attnd duty $treatment $xlist, pscore(pscore) comsup boot reps($breps) dots


{res} The program is searching the nearest neighbor of each treated unit. 
 This operation may take a while.



{col 1}ATT estimation with Nearest Neighbor Matching method 
{col 1}(random draw version)
{col 1}Analytical standard errors

{txt}{hline 57}
{col 1}n. treat.{col 13}n. contr.{col 25}      ATT{col 38}Std. Err.{col 49}        t
{hline 57}

{col 1}{res}      102{col 13}      107{col 25}    0.129{col 38}    0.074{col 49}    1.749

{txt}{hline 57}
Note: the numbers of treated and controls refer to actual
nearest neighbour matches





Bootstrapping of standard errors 

command:{col 15}attnd duty treatment ageyrs female nectime educ if pre_finish!=. , pscore(pscore) comsup
statistic:{col 15}attnd{col 25} = r(attnd)
....................................................................................................


Bootstrap statistics{col 51}Number of obs    ={res}      2097
{txt}{col 51}Replications     ={res}       100

{txt}{hline 13}{c TT}{hline 64}
Variable     {c |}  Reps  Observed      Bias  Std. Err. [95% Conf. Interval]
{hline 13}{c +}{hline 64}
       attnd {c |}{res}   100   .129099  .0128415  .0893816  -.0482535   .3064514   {txt}(N)
{col 13} {c |}{res}{col 53} -.006701    .331716   {txt}(P)
{col 13} {c |}{res}{col 53}-.0302778   .3279461  {txt}(BC)
{hline 13}{c BT}{hline 64}
Note:{col 8}N   = normal
{col 8}P   = percentile
{col 8}BC  = bias-corrected



{col 1}{res}ATT estimation with Nearest Neighbor Matching method
{col 1}(random draw version)
{col 1}Bootstrapped standard errors

{txt}{hline 57}
{col 1}n. treat.{col 13}n. contr.{col 25}      ATT{col 37}Std. Err.{col 49}        t
{hline 57}

{col 1}{res}      102{col 13}      107{col 25}    0.129{col 37}    0.089{col 49}    1.444

{txt}{hline 57}
Note: the numbers of treated and controls refer to actual
nearest neighbour matches

{com}. attnd vote $treatment $xlist, pscore(pscore) comsup boot reps($breps) dots


{res} The program is searching the nearest neighbor of each treated unit. 
 This operation may take a while.



{col 1}ATT estimation with Nearest Neighbor Matching method 
{col 1}(random draw version)
{col 1}Analytical standard errors

{txt}{hline 57}
{col 1}n. treat.{col 13}n. contr.{col 25}      ATT{col 38}Std. Err.{col 49}        t
{hline 57}

{col 1}{res}      102{col 13}      108{col 25}    0.051{col 38}    0.039{col 49}    1.296

{txt}{hline 57}
Note: the numbers of treated and controls refer to actual
nearest neighbour matches





Bootstrapping of standard errors 

command:{col 15}attnd vote treatment ageyrs female nectime educ if pre_finish!=. , pscore(pscore) comsup
statistic:{col 15}attnd{col 25} = r(attnd)
....................................................................................................


Bootstrap statistics{col 51}Number of obs    ={res}      2097
{txt}{col 51}Replications     ={res}       100

{txt}{hline 13}{c TT}{hline 64}
Variable     {c |}  Reps  Observed      Bias  Std. Err. [95% Conf. Interval]
{hline 13}{c +}{hline 64}
       attnd {c |}{res}   100  .0510621 -.0164351  .0457137  -.0396438    .141768   {txt}(N)
{col 13} {c |}{res}{col 53}-.0408163    .152648   {txt}(P)
{col 13} {c |}{res}{col 53} -.009434   .1885125  {txt}(BC)
{hline 13}{c BT}{hline 64}
Note:{col 8}N   = normal
{col 8}P   = percentile
{col 8}BC  = bias-corrected



{col 1}{res}ATT estimation with Nearest Neighbor Matching method
{col 1}(random draw version)
{col 1}Bootstrapped standard errors

{txt}{hline 57}
{col 1}n. treat.{col 13}n. contr.{col 25}      ATT{col 37}Std. Err.{col 49}        t
{hline 57}

{col 1}{res}      102{col 13}      108{col 25}    0.051{col 37}    0.046{col 49}    1.117

{txt}{hline 57}
Note: the numbers of treated and controls refer to actual
nearest neighbour matches

{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/aramhur/Desktop/untitled folder/bjps koreaexp_output.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 7 Feb 2018, 10:03:27
{txt}{.-}
{smcl}
{txt}{sf}{ul off}